This article introduces Streamlit, a Python library for building data dashboards, as a solution for Python programmers to create graphical front-ends without needing to delve into CSS, HTML, or JavaScript. The author, a seasoned data engineer, explains how Streamlit and similar tools enable the creation of attractive dashboards, marking a shift from traditional tools like Tableau or Quicksight. This piece serves as the first in a series focusing on Streamlit, with future articles planned on Gradio and Taipy. The author aims to replicate similar layouts and functionalities across dashboards using consistent data.
SHREC is a physics-based unsupervised learning framework that reconstructs unobserved causal drivers from complex time series data. This new approach addresses the limitations of contemporary techniques, such as noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful application of SHREC on diverse datasets highlights its wide applicability and reliability in fields like biology, physics, and engineering, improving the accuracy of causal driver reconstruction.
Writer/director Steven Conrad has created a podcast that serves as an audiobook for 'The Integral Principles of the Structural Dynamics of Flow,' with narration by Kurtwood Smith, who played the book's author, L.G. Claret, in 'Patriot.' The podcast offers a backstory of the flawed structural engineer Leslie Claret, enhanced with music from Conrad's band, The Jones Sisters, which also features in his Epix series 'Perpetual Grace LTD.'
The Algarve region in southern Portugal is highlighted by Rick Steves as the 'Land's End of Europe.' Known for its stunning beaches, excellent weather, delicious food, and vibrant culture, the Algarve offers a perfect warm-weather destination. Notable attractions include Cape Saint Vincent, a key geographical point, and towns like Lagos, Burgau, and Sagres, each offering unique experiences from beautiful beaches to rich historical sites. The area is also celebrated for its seafood, wine, and artisan crafts.
This speculative article explores the idea that GPT-5 might already exist internally at OpenAI but is being withheld from public release due to cost and performance considerations. It draws parallels with Anthropic's handling of a similar situation with Claude Opus 3.5, suggesting that both companies might be using larger models internally to improve smaller models without incurring high public-facing costs. The author examines the potential motivations behind such decisions, including cost control, performance expectations, and strategic partnerships.
History-based Feature Selection (HBFS) is a feature selection tool that aims to identify an optimal subset of features for prediction problems. It is designed to work similarly to wrapper methods and genetic methods, focusing on selecting feature subsets that yield the highest performance for a given dataset and target. HBFS differs from filter methods, which evaluate and rank individual features based on their predictive power. Instead, HBFS evaluates combinations of features over multiple iterations, using a Random Forest regressor to estimate performance and iteratively refining feature sets. This tool supports binary and multiclass classification, as well as regression, and allows for balancing the trade-off between maximizing accuracy and minimizing the number of features through parameters such as maximum features and penalties. Examples provided demonstrate the use of HBFS with various models and metrics, showcasing its ability to improve model performance by identifying optimal feature subsets.
Researchers at UC Berkeley have developed Sky-T1-32B, an open-source reasoning-focused language model trained for less than $450, which surpasses OpenAI's o1 in benchmarks like Math500, AIME, and Livebench. This model uses optimized training processes to balance computational efficiency with robust performance, making it accessible to a broader audience and fostering inclusivity in AI research.
Researchers discovered long-lost computer code and used it to resurrect the early chatbot ELIZA from MIT. Named after Eliza Doolittle from 'Pygmalion,' ELIZA was developed in the 1960s by MIT professor Joseph Weizenbaum. It was designed to emulate a psychotherapist in conversation and used a unique programming language called MAD-SLIP. Rediscovered in 2021, the original code was brought back to life after 60 years, demonstrating the chatbot's functionality and highlighting the historical significance of early artificial intelligence.